Analyte Sensor with Time Lag Compensation

ABSTRACT

Methods and devices and systems for determining an analyte value are disclosed.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/924,528 filed Jun. 21, 2013, now U.S. Pat. No. 9,320,468,which is a continuation of U.S. patent application Ser. No. 12/363,706filed Jan. 30, 2009, now U.S. Pat. No. 8,473,022, which claims priorityunder §35 U.S.C. 119(e) to U.S. Provisional Application No. 61/025,290filed Jan. 31, 2008, and is related to U.S. patent application Ser. No.11/537,991 filed on Oct. 2, 2006, now U.S. Pat. No. 7,618,369, eachassigned to the Assignee of the present application Abbott Diabetes CareInc., of Alameda, Calif., the disclosures of each of which areincorporated herein by reference for all purposes.

BACKGROUND

Analyte, e.g., glucose monitoring systems including continuous anddiscrete monitoring systems generally include a small, lightweightbattery powered and microprocessor controlled system which is configuredto detect signals proportional to the corresponding measured glucoselevels using an electrometer, and RF signals to transmit the collecteddata. One aspect of certain analyte monitoring systems include atranscutaneous or subcutaneous analyte sensor configuration which is,for example, partially mounted on the skin of a subject whose analytelevel is to be monitored. The sensor cell may use a two orthree-electrode (work, reference and counter electrodes) configurationdriven by a controlled potential (potentiostat) analog circuit connectedthrough a contact system.

The analyte sensor may be configured so that at least a portion thereofis placed under the skin of the patient so as to detect the analytelevels of the patient. In embodiments in which a portion is below theskin and a portion is above, the portion above the skin may be directlyor indirectly connected with the transmitter unit. The transmitter unitis configured to transmit the analyte levels, e.g., in the form ofcurrent, detected by the sensor over a wireless (or wired) communicationlink such as an RF (radio frequency) communication link to areceiver/monitor unit. The receiver/monitor unit performs data analysis,among others on the received analyte levels to generate informationpertaining to the monitored analyte levels.

To obtain accurate data from the analyte sensor, calibration may benecessary. In certain instances, blood glucose measurements areperiodically obtained using, for example, a conventional analyte teststrip and blood glucose meter, and the measured blood glucose values areused to calibrate the sensors. Indeed, the patient may calibrate eachnew analyte sensor using for example, capillary blood glucosemeasurements. Due to a lag factor between the monitored data and themeasured blood glucose values, an error may be introduced in themonitored data.

In view of the foregoing, it would be desirable to have a method andsystem for calibrating analyte sensors of an analyte monitoring systemto account for such lag errors in analyte monitoring systems.

SUMMARY

In one embodiment, methods including determining a calibration parameterassociated with a detected analyte value, calibrating the analyte valuebased on the calibration parameter, and dynamically updating thecalibration parameter is disclosed. Devices, systems and algorithms(e.g., embodied on computer readable medium) for performing such methodsare also provided.

These and other objects, features and advantages of the presentdisclosure will become more fully apparent from the following detaileddescription of the embodiments, the appended claims and the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a data monitoring and managementsystem for practicing one or more embodiments of the present disclosure;

FIG. 2 is a block diagram of the transmitter unit of the data monitoringand management system shown in FIG. 1 in accordance with one embodimentof the present disclosure;

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure;

FIG. 4 is a flowchart illustrating an overall dynamically updatingcalibration in accordance with one embodiment of the present disclosure;

FIG. 5 is a flowchart illustrating the lag correction and calibrationroutine of the overall dynamically updating calibration shown in FIG. 4in accordance with one embodiment of the present disclosure;

FIG. 6 is a flowchart illustrating the lag correction and dynamicallyupdating calibration routine of the overall dynamically updatingcalibration shown in FIG. 4 in accordance with one embodiment of thepresent disclosure;

FIG. 7 is a flowchart illustrating a method for estimating the lag timeconstant from sensor analyte data and reference measurements inaccordance with one embodiment of the present disclosure; and

FIG. 8 is a flowchart illustrating lag time constant compensated analytesensor data in accordance with one embodiment of the present disclosure.

DETAILED DESCRIPTION

As described in further detail below, in accordance with the variousembodiments of the present disclosure, there is provided a method andsystem for calibration of analyte sensors to reduce errors in the sensormeasurements. In particular, within the scope of the present disclosure,there are provided method and system for calibrating subcutaneous ortranscutaneously positioned analyte sensors to compensate for time lagerrors associated with an analyte sensor.

FIG. 1 illustrates a data monitoring and management system such as, forexample, analyte (e.g., glucose) monitoring system 100 in accordancewith one embodiment of the present disclosure. The subject invention isfurther described primarily with respect to a glucose monitoring systemfor convenience and such description is in no way intended to limit thescope of the invention. It is to be understood that the analytemonitoring system may be configured to monitor a variety of analytes,e.g., lactate, and the like. For example, analytes that may be monitoredinclude but are not limited to, for example, acetyl choline, amylase,bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g.,CK-MB), creatine, DNA, fructosamine, glucose, glutamine, growthhormones, hormones, ketones, lactate, peroxide, prostate-specificantigen, prothrombin, RNA, thyroid stimulating hormone, and troponin.The concentration of drugs, such as, for example, antibiotics (e.g.,gentamicin, vancomycin, and the like), digitoxin, digoxin, drugs ofabuse, theophylline, and warfarin, may also be monitored.

The analyte monitoring system 100 includes a sensor 101, a transmitterunit 102 directly or indirectly coupled to the sensor 101, and a primaryreceiver unit 104 which is configured to communicate with thetransmitter unit 102 via a communication link 103. The primary receiverunit 104 may be further configured to transmit data to a data processingterminal 105 for evaluating the data received by the primary receiverunit 104. Moreover, the data processing terminal in one embodiment maybe configured to receive data directly from the transmitter unit 102 viaa communication link which may optionally be configured forbi-directional communication.

Also shown in FIG. 1 is an optional secondary receiver unit 106 which isoperatively coupled to the communication link and configured to receivedata transmitted from the transmitter unit 102. Moreover, as shown inthe Figure, the secondary receiver unit 106 is configured to communicatewith the primary receiver unit 104 as well as the data processingterminal 105. Indeed, the secondary receiver unit 106 may be configuredfor bi-directional wireless communication with each of the primaryreceiver unit 104 and the data processing terminal 105. As discussed infurther detail below, in one embodiment of the present disclosure, thesecondary receiver unit 106 may be configured to include a limitednumber of functions and features as compared with the primary receiverunit 104. As such, the secondary receiver unit 106 may be configuredsubstantially in a smaller compact housing or embodied in a device suchas a wrist watch, for example. Alternatively, the secondary receiverunit 106 may be configured with the same or substantially similarfunctionality as the primary receiver unit 104, and may be configured tobe used in conjunction with a docking cradle unit for placement bybedside, for night time monitoring, and/or bi-directional communicationdevice.

Only one sensor 101, transmitter unit 102, communication link 103, anddata processing terminal 105 are shown in the embodiment of the analytemonitoring system 100 illustrated in FIG. 1. However, it will beappreciated by one of ordinary skill in the art that the analytemonitoring system 100 may include one or more sensor 101, transmitterunit 102, communication link 103, and data processing terminal 105.Moreover, within the scope of the present disclosure, the analytemonitoring system 100 may be a continuous monitoring system, orsemi-continuous, or a discrete monitoring system. In a multi-componentenvironment, each device is configured to be uniquely identified by eachof the other devices in the system so that communication conflict isreadily resolved between the various components within the analytemonitoring system 100.

In one embodiment of the present disclosure, the sensor 101 isphysically positioned in or on the body of a user whose analyte level isbeing monitored. In one aspect, the sensor 101 may be configured to useone or more of coulometric, amperometric, potentiometric orconductimetric approaches to measure the analyte level being monitored.The sensor 101 may be configured to continuously sample the analyte ofthe user and the sampled analyte may be converted into a correspondingdata signal for transmission by the transmitter unit 102. In oneembodiment, the transmitter unit 102 is mounted on the sensor 101 sothat both devices are positioned on the user's body. The transmitterunit 102 performs data processing such as filtering and encoding on datasignals, each of which corresponds to a sampled analyte level of theuser, for transmission to the primary receiver unit 104 via thecommunication link 103.

In one embodiment, the analyte monitoring system 100 is configured as aone-way RF communication path from the transmitter unit 102 to theprimary receiver unit 104. In such embodiment, the transmitter unit 102may transmit the sampled data signals received from the sensor 101without acknowledgement from the primary receiver unit 104 that thetransmitted sampled data signals have been received (in otherembodiments there may be acknowledgement). For example, the transmitterunit 102 may be configured to transmit the encoded sampled data signalsat a fixed rate (e.g., at one minute intervals or other interval) afterthe completion of the initial power on procedure. Likewise, the primaryreceiver unit 104 may be configured to detect such transmitted encodedsampled data signals at predetermined time intervals. Alternatively, theanalyte monitoring system 100 may be configured with a bi-directional RF(or otherwise) communication between the transmitter unit 102 and theprimary receiver unit 104.

Additionally, in one aspect, the primary receiver unit 104 may includetwo sections. The first section is an analog interface section that isconfigured to communicate with the transmitter unit 102 via thecommunication link 103. In one embodiment, the analog interface sectionmay include an RF receiver and an antenna for receiving and amplifyingthe data signals from the transmitter unit 102, which are thereafter,demodulated with a local oscillator and filtered through a band-passfilter. The second section of the primary receiver unit 104 is a dataprocessing section which is configured to process the data signalsreceived from the transmitter unit 102 such as by performing datadecoding, error detection and correction, data clock generation, anddata bit recovery.

In operation in certain embodiments, upon completing a power-onprocedure if required, the primary receiver unit 104 is configured todetect the presence of the transmitter unit 102 within its range basedon, for example, the strength of the detected data signals received fromthe transmitter unit 102 or a predetermined transmitter identificationinformation. Upon successful synchronization with the correspondingtransmitter unit 102, the primary receiver unit 104 is configured tobegin receiving from the transmitter unit 102 data signals correspondingto the user's detected analyte level. More specifically, the primaryreceiver unit 104 in one embodiment is configured to performsynchronized time hopping with the corresponding synchronizedtransmitter unit 102 via the communication link 103 to obtain the user'sdetected analyte level.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs), telephone such as acellular telephone), and the like, each of which may be configured fordata communication with the receiver via a wired or a wirelessconnection. Additionally, the data processing terminal 105 may furtherbe connected to a data network (not shown) for storing, retrieving andupdating data corresponding to the detected analyte level of the user.

Within the scope of the present disclosure, the data processing terminal105 may include an infusion device such as an insulin infusion pump orthe like, which may be configured to administer a drug such as, forexample insulin, to users, and which may be configured to communicatewith the receiver unit 104 for receiving, among others, the measuredanalyte level. Alternatively, the receiver unit 104 may be configured tointegrate an infusion device therein so that the receiver unit 104 isconfigured to administer insulin therapy to patients, for example, foradministering and modifying basal profiles, as well as for determiningappropriate boluses for administration based on, among others, thedetected analyte levels received from the transmitter unit 102.

Additionally, the transmitter unit 102, the primary receiver unit 104and the data processing terminal 105 may each be configured forbi-directional wireless communication such that each of the transmitterunit 102, the primary receiver unit 104 and the data processing terminal105 may be configured to communicate (that is, transmit data to andreceive data from) with each other via the wireless communication link103. More specifically, the data processing terminal 105 may in oneembodiment be configured to receive data directly from the transmitterunit 102 via a communication link, where the communication link, asdescribed above, may be configured for bi-directional communication.

In this embodiment, the data processing terminal 105 which may includean insulin pump, may be configured to receive the analyte signals fromthe transmitter unit 102, and thus, incorporate the functions of thereceiver unit 104 including data processing for managing the patient'sinsulin therapy and analyte monitoring. In one embodiment, thecommunication link 103 may include one or more of an RF communicationprotocol, an infrared communication protocol, a Bluetooth® enabledcommunication protocol, an 802.11x wireless communication protocol, oran equivalent wireless communication protocol which would allow secure,wireless communication of several units (for example, per HIPAArequirements) while avoiding potential data collision and interference.

FIG. 2 is a block diagram of the transmitter of the data monitoring anddetection system shown in FIG. 1 in accordance with one embodiment ofthe present disclosure. Referring to the Figure, the transmitter unit102 in one embodiment includes an analog interface 201 configured tocommunicate with the sensor 101 (FIG. 1), a user input 202, and atemperature detection section 203, each of which is operatively coupledto a transmitter processor 204 such as a central processing unit (CPU).As can be seen from FIG. 2, there are provided four contacts, three ofwhich are electrodes—work electrode (W) 210, guard contact (G) 211,reference electrode (R) 212, and counter electrode (C) 213, eachoperatively coupled to the analog interface 201 of the transmitter unit102 for connection to the sensor 101 (FIG. 1). In one embodiment, eachof the work electrode (W) 210, guard contact (G) 211, referenceelectrode (R) 212, and counter electrode (C) 213 may be made using aconductive material that may be applied in any suitable manner, e.g.,printed or etched, for example, such as carbon, gold, and the like,which may be printed, or metal foil (e.g., gold) which may be etched.

Further shown in FIG. 2 are a transmitter serial communication section205 and an RF transmitter 206, each of which is also operatively coupledto the transmitter processor 204. Moreover, a power supply 207 such as abattery is also provided in the transmitter unit 102 to provide thenecessary power for the transmitter unit 102. Additionally, as can beseen from the Figure, clock 208 is provided to, among others, supplyreal time information to the transmitter processor 204.

In one embodiment, a unidirectional input path is established from thesensor 101 (FIG. 1) and/or manufacturing and testing equipment to theanalog interface 201 of the transmitter unit 102, while a unidirectionaloutput is established from the output of the RF transmitter 206 of thetransmitter unit 102 for transmission to the primary receiver unit 104.In this manner, a data path is shown in FIG. 2 between theaforementioned unidirectional input and output via a dedicated link 209from the analog interface 201 to serial communication section 205,thereafter to the processor 204, and then to the RF transmitter 206. Assuch, in one embodiment, via the data path described above, thetransmitter unit 102 is configured to transmit to the primary receiverunit 104 (FIG. 1), via the communication link 103 (FIG. 1), processedand encoded data signals received from the sensor 101 (FIG. 1).Additionally, the unidirectional communication data path between theanalog interface 201 and the RF transmitter 206 discussed above allowsfor the configuration of the transmitter unit 102 for operation uponcompletion of the manufacturing process as well as for directcommunication for diagnostic and testing purposes.

As discussed above, the transmitter processor 204 is configured totransmit control signals to the various sections of the transmitter unit102 during the operation of the transmitter unit 102. In one embodiment,the transmitter processor 204 also includes a memory (not shown) forstoring data such as the identification information for the transmitterunit 102, as well as the data signals received from the sensor 101. Thestored information may be retrieved and processed for transmission tothe primary receiver unit 104 under the control of the transmitterprocessor 204. Furthermore, the power supply 207 may include acommercially available battery.

The transmitter unit 102 is also configured such that the power supplysection 207 is capable of providing power to the transmitter for apredetermined minimum continuous operation time period and also with apredetermined minimum shelf life time period such as, for example, aminimum of about three months of continuous operation after having beenstored for about eighteen months in a low-power (non-operating) mode. Inone embodiment, this may be achieved by the transmitter processor 204operating in low power modes in the non-operating state, for example,drawing no more than approximately 1 μA of current. Indeed, in oneembodiment, the final step during the manufacturing process of thetransmitter unit 102 may place the transmitter unit 102 in the lowerpower, non-operating state (i.e., post-manufacture sleep mode). In thismanner, the shelf life of the transmitter unit 102 may be significantlyimproved. Moreover, as shown in FIG. 2, while the power supply unit 207is shown as coupled to the processor 204, and as such, the processor 204is configured to provide control of the power supply unit 207, it shouldbe noted that within the scope of the present disclosure, the powersupply unit 207 is configured to provide the necessary power to each ofthe components of the transmitter unit 102 shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of thetransmitter unit 102 in one embodiment may include a rechargeablebattery unit that may be recharged by a separate power supply rechargingunit (for example, provided in the receiver unit 104) so that thetransmitter unit 102 may be powered for a longer period of usage time.Moreover, in one embodiment, the transmitter unit 102 may be configuredwithout a battery in the power supply section 207, in which case thetransmitter unit 102 may be configured to receive power from an externalpower supply source (for example, a battery) as discussed in furtherdetail below.

Referring yet again to FIG. 2, the optional temperature detectionsection 203 of the transmitter unit 102 is configured to monitor thetemperature of the skin near the sensor insertion site. The temperaturereading may be used to adjust the analyte readings obtained from theanalog interface 201. The RF transmitter 206 of the transmitter unit 102may be configured for operation in the frequency band of 315 MHz to 322MHz, for example, in the United States. Further, in one embodiment, theRF transmitter 206 is configured to modulate the carrier frequency byperforming Frequency Shift Keying and Manchester encoding. In oneembodiment, the data transmission rate is 19,200 symbols per second,with a minimum transmission range for communication with the primaryreceiver unit 104.

Referring yet again to FIG. 2, also shown is a leak detection circuit214 coupled to the guard contact (G) 211 and the processor 204 in thetransmitter unit 102 of the data monitoring and management system 100.The leak detection circuit 214 in accordance with one embodiment of thepresent disclosure may be configured to detect leakage current in thesensor 101 to determine whether the measured sensor data is corrupt orwhether the measured data from the sensor 101 is accurate.

Additional detailed description of the continuous analyte monitoringsystem, its various components including the functional descriptions ofthe transmitter are provided in U.S. Pat. No. 6,175,752 issued Jan. 16,2001 entitled “Analyte Monitoring Device and Methods of Use”, and inU.S. application Ser. No. 10/745,878 filed Dec. 26, 2003, now U.S. Pat.No. 7,811,231, entitled “Continuous Glucose Monitoring System andMethods of Use”, each assigned to the Assignee of the presentapplication.

FIG. 3 is a block diagram of the receiver/monitor unit of the datamonitoring and management system shown in FIG. 1 in accordance with oneembodiment of the present disclosure. Referring to FIG. 3, the primaryreceiver unit 104 includes a blood glucose test strip interface 301, anRF receiver 302, an input 303, a temperature detection section 304, anda clock 305, each of which is operatively coupled to a receiverprocessor 307. As can be further seen from the Figure, the primaryreceiver unit 104 also includes a power supply 306 operatively coupledto a power conversion and monitoring section 308. Further, the powerconversion and monitoring section 308 is also coupled to the receiverprocessor 307. Moreover, also shown are a receiver serial communicationsection 309, and an output 310, each operatively coupled to the receiverprocessor 307.

In one embodiment, the test strip interface 301 includes a glucose leveltesting portion to receive a manual insertion of a glucose test strip,and thereby determine and display the glucose level of the test strip onthe output 310 of the primary receiver unit 104. This manual testing ofglucose can be used to calibrate sensor 101. The RF receiver 302 isconfigured to communicate, via the communication link 103 (FIG. 1) withthe RF transmitter 206 of the transmitter unit 102, to receive encodeddata signals from the transmitter unit 102 for, among others, signalmixing, demodulation, and other data processing. The input 303 of theprimary receiver unit 104 is configured to allow the user to enterinformation into the primary receiver unit 104 as needed. In one aspect,the input 303 may include one or more keys of a keypad, atouch-sensitive screen, or a voice-activated input command unit. Thetemperature detection section 304 is configured to provide temperatureinformation of the primary receiver unit 104 to the receiver processor307, while the clock 305 provides, among others, real time informationto the receiver processor 307.

Each of the various components of the primary receiver unit 104 shown inFIG. 3 is powered by the power supply 306 which, in one embodiment,includes a battery. Furthermore, the power conversion and monitoringsection 308 is configured to monitor the power usage by the variouscomponents in the primary receiver unit 104 for effective powermanagement and to alert the user, for example, in the event of powerusage which renders the primary receiver unit 104 in sub-optimaloperating conditions. An example of such sub-optimal operating conditionmay include, for example, operating the vibration output mode (asdiscussed below) for a period of time thus substantially draining thepower supply 306 while the processor 307 (thus, the primary receiverunit 104) is turned on. Moreover, the power conversion and monitoringsection 308 may additionally be configured to include a reverse polarityprotection circuit such as a field effect transistor (FET) configured asa battery activated switch.

The serial communication section 309 in the primary receiver unit 104 isconfigured to provide a bi-directional communication path from thetesting and/or manufacturing equipment for, among others,initialization, testing, and configuration of the primary receiver unit104. Serial communication section 309 can also be used to upload data toa computer, such as time-stamped blood glucose data. The communicationlink with an external device (not shown) can be made, for example, bycable, infrared (IR) or RF link. The output 310 of the primary receiverunit 104 is configured to provide, among others, a graphical userinterface (GUI) such as a liquid crystal display (LCD) for displayinginformation. Additionally, the output 310 may also include an integratedspeaker for outputting audible signals as well as to provide vibrationoutput as commonly found in handheld electronic devices, such as mobiletelephones presently available. In a further embodiment, the primaryreceiver unit 104 also includes an electro-luminescent lamp configuredto provide backlighting to the output 310 for output visual display indark ambient surroundings.

Referring back to FIG. 3, the primary receiver unit 104 in oneembodiment may also include a storage section such as a programmable,non-volatile memory device as part of the processor 307, or providedseparately in the primary receiver unit 104, operatively coupled to theprocessor 307. The processor 307 may be configured to perform Manchesterdecoding as well as error detection and correction upon the encoded datasignals received from the transmitter unit 102 via the communicationlink 103.

FIG. 4 is a flowchart illustrating an overall dynamically updatingcalibration in accordance with various embodiments of the presentdisclosure. Referring to FIG. 4, a counter such as a calibration counteris triggered to perform calibration of the monitored data such as theanalyte data received from the transmitter unit 102 (FIG. 1). In oneaspect, the calibration may be performed on calibrated data, or on theuncalibrated data, for example, on the analyte data received prior tothe initial calibration performed. In one embodiment, the calibrationcounter may include a timer or a clock which may be configured to promptthe user or the patient to initiate the acquisition of reference data(e.g., blood glucose reference data, e.g., obtained using a teststrip/meter system) at predetermined time intervals. When thecalibration counter is initially triggered, the time counter T isinitialized to zero (0). Thereafter, a calibration parameter isdetermined based on, for example, the acquired reference data and themonitored sensor data at time T=0. Moreover, in one embodiment, themonitored sensor data may be updated based on the calibration parameter.In one embodiment, the calibration parameter may include a sensorsensitivity value associated with the analyte sensor 101 (FIG. 1)configured to monitor the analyte levels of the patient.

As described in further detail below, for example, in conjunction withFIG. 5, in particular embodiments, during the initial calibration stageat T=0, a reference glucose value is determined, for example, such as acapillary blood glucose value using a blood glucose determination systemsuch as Freestyle® blood glucose monitoring system or Precision® bloodglucose monitoring system available from Abbott Diabetes Care Inc.,Alameda, Calif. In certain embodiments, an analyte meter may beintegrated into the receiver unit. In addition, the monitored sensordata at or near the calibration time (T=0) is retrieved which mayinclude the monitored sensor data at time T=T−1, at time T=T+1, or anyother suitable time period (for example, from the processing and storageunit 307 (FIG. 3) of the receiver unit 104 (FIG. 1)).

More specifically, in one embodiment, the monitored sensor data at thecalibration time (T=0) may include one or more monitored sensor data inaddition to the monitored sensor data point at the calibration time(T=0). That is, in one embodiment, the monitored sensor data at thecalibration time (T=0) may include all monitored sensor data availablefor retrieval from the receiver unit 104 (FIG. 1) at the calibrationtime (T=0). For example, to reduce the contribution of noise in themeasured sensor data, an average of the two most recent sensor data maybe associated with the monitored sensor data at the calibration time(T=0).

The monitored sensor data at a predetermined time may include, inparticular embodiments, an estimate of the sensor data at thepredetermined time as determined by the one or more filters which may beconfigured to use the monitored sensor data up to and including the datapoint at the predetermined time (for example, up to the data point atcalibration time (T=0)). In one embodiment, one or more filters such asa finite impulse response (FIR) filter may be used to determine the bestestimate at a predetermined time using a finite window of monitoredsensor data up to the current or most recent monitored sensor datapoint.

Referring back to FIG. 4, after determining the calibration parameterand updating the monitored data at the calibration time (T=0), thecounter is incremented by one (1), and dynamic, real-time update of thecalibration parameter is performed. In one embodiment, the counter maybe configured to increment by one with each reception of sensor datafrom the transmitter unit 102 (FIG. 1). After dynamically updating thecalibration parameter at the subsequent incremented time (T=1), it isdetermined whether the counter has reached a predetermined count (forexample, set at seven (7)). If it is determined that the counter has notreached the predetermined count, then the routine in one embodimentreturns to step 430 where the counter is incremented by one (1) and thedynamically updating calibration parameter and monitored sensor data isperformed for monitored data at the second subsequent incremented time(T=2).

On the other hand, if it is determined that the counter has reached thepredetermined count, then in one embodiment, subsequent monitored sensordata may be updated based on the dynamically updated calibrationparameter and/or updated monitored sensor data. Thereafter, inparticular embodiments, it is determined whether further or subsequentlag correction will likely not yield more accurate monitored data values(or with less errors). Therefore, in one embodiment, the routineterminates and awaits for the subsequent calibration time, for example,to repeat the processes described above in conjunction with FIG. 4. Whena procedure such as described in FIG. 4 has been successfully completed,routines such as determining lag time constant based on calibratedsensor data 740 (FIG. 7) may be updated in order to obtain a betterestimate.

In this manner, there are provided methods and system for dynamically,and in particular embodiments, in real-time, obtaining reference data ata first predetermined time, receiving measured data prior to andincluding (or near) the first predetermined time, calculating a firstcalibration parameter (or parameters) using the data, calibrating themeasured data based on the calibration parameter, receiving measureddata at a second predetermined time, updating the calibration parameterbased on all of the previous data and the newly received measured data,calibrating the newly received measured data based on the updatedcalibration parameter, and repeating a number of times the process ofreceiving new measurement data, updating the calibration parameter,calibrating the newly received measurement data, and calibrating anynewly received measurement data with the fully updated calibrationparameter.

A method in a further embodiment may include performing lag compensationon the measured data that is used to update the calibration parameter.Lag compensation may optionally be performed on the measured data thatis calibrated or on uncalibrated data. A method in a further embodimentincludes filtering the measured data that is used to update thecalibration parameter.

FIG. 5 is a flowchart illustrating the lag correction and calibrationroutine of the overall dynamically updating calibration shown in FIG. 4in accordance with one embodiment of the present disclosure. Referringto FIG. 5, the determination of calibration parameter and updating themonitored analyte level at the calibration time (T=0) is described infurther detail. More specifically, in one embodiment, a capillary bloodglucose value is determined at the calibration time (T=0), and themonitored analyte value at the calibration time is retrieved from thereceiver unit 104 of the monitoring system 100 (FIG. 1).

Thereafter, a rate of change of the monitored data at the calibrationtime (T=0) is determined. In one embodiment, the rate of change of themonitored data at the calibration time (T=0) may be determined using oneor more filters including, but not limited to, infinite impulse response(IIR) filter, finite impulse response (FIR) filter, backward and/orforward smoothing techniques (e.g., Kalman filtering technique), or anyother equivalent one or more causal filters that balance signal noisereduction with lag correction.

Upon determining the rate of change of the monitored data at thecalibration time (T=0), the monitored data at the calibration time (T=0)is updated. In one embodiment, the updated monitored sensor data mayinclude lag corrected monitored data at the calibration time (T=0).Optionally, the lag correction for the monitored data at the calibrationtime (T=0) may be skipped and not performed. In one embodiment, the lagcorrected monitored data at the calibration time (T=0) may be determinedby applying the determined rate of change of the monitored data at thecalibration time (T=0) to a predetermined constant value. In oneembodiment, the predetermined constant value may include, apredetermined time constant.

For example, in one embodiment, the predetermined time constant mayinclude a fixed time constant in the range of approximately four tofifteen minutes, and which may be associated with the one or more of thepatient physiological profile, one or more attributes associated withthe monitoring system 100 (including, for example but not limited to,the characteristics of the analyte sensor 101). In a further aspect, thepredetermined time constant may vary based on one or more factorsincluding, for example, but not limited to the timing and amount of foodintake by the patient, exogenous insulin intake, physical activities bythe patient such as exercise, or any other factors that may affect thetime constant, and which may be empirically determined.

Referring again to FIG. 5, the calibration parameter (for example, thesensitivity of the analyte sensor 101 FIG. 1), may be determined forexample, in one embodiment, by determining the ratio of the monitoreddata (optionally lag corrected) at the calibration time (T=0) and thereference data obtained using, for example, the blood glucose meter asdescribed above. In one embodiment, the calibration parameter may bedetermined by dividing the monitored data at the calibration time (T=0)by the reference data such as the capillary blood glucose value at thecalibration time (T=0).

Thereafter, in one embodiment, the calibrated and updated monitoredsensor data at the calibration time (T=0) is determined based upon themonitored data (optionally lag corrected) and the calibration parameteras determined above. For example, in one embodiment, the calibrated andupdated monitored sensor data at the calibration time (T=0) may bedetermined by dividing the lag corrected monitored data at calibrationtime (T=0) by the determined calibration parameter.

FIG. 6 is a flowchart illustrating the lag correction and dynamicallyupdating calibration routine of the overall dynamically updatingcalibration shown in FIG. 4 in accordance with one embodiment of thepresent disclosure. Referring to FIGS. 4 and 6, with the counterincremented by one (see step 430 of FIG. 4 and step 610 b of FIG. 6),the analyte value at the subsequent incremented time (T=1) is retrievedfrom, for example, the processing and storage unit 307 (FIG. 3) of thereceiver unit 104. In particular, in one embodiment, the rate of changeof the monitored data at the calibration time (T=0) is updated based onthe monitored data value at the subsequent incremented time (T=1). Inother words, the process starts with the determination of glucose rateof change at calibration time (T=0) 610 in FIG. 6. At the next timeincrement, this value is revised using the monitored data values atcalibration time (T=0) and prior data and at the subsequent incrementedtime (T=1) (see 620 and 630 of FIG. 6), the rate of change of themonitored data at the calibration time (T=0) may be estimated with animproved accuracy at T=1 (see 640 of FIG. 6). Again, in one embodiment,the rate of change may be determined based on one or more of, but notlimited to, infinite impulse response (IIR) filter, finite impulseresponse (FIR) filter, backward and/or forward smoothing techniques(e.g., Kalman filtering technique), or any other equivalent filtering orsmoothing techniques.

With the updated rate of change at the calibration time (T=0)determined, monitored data (optionally lag corrected) at calibrationtime (T=0) is updated. That is, in one embodiment, the lag correctedsensor data at the calibration time (T=0) is updated based on the priorlag corrected and calibrated data at calibration time (T=0), and inconjunction with the predetermined constant (for example, thepredetermined time constant discussed above), and the updated rate ofchange of the monitored data at the calibration time (T=0). For example,in one embodiment, the lag corrected monitored data at the calibrationtime (T=0) is updated or determined by taking the sum of the lagcorrected and calibration sensor value at calibration time (T=0) asdetermined above, with the updated rate of change of monitored data atcalibration time (T=0) multiplied by the predetermined constant. Inother words, in one embodiment, the updated rate of change of themonitored data at calibration time (T=0) may be multiplied by thepredetermined constant, and thereafter, the resulting value is added tothe lag corrected and calibrated monitored data at the calibration time(T=0) previously determined (see for example, step 420).

Referring again to FIG. 6, after determining the updated lag correctedmonitored data at calibration time (T=0) based on monitored data at thesubsequent incremented time (T=1) as described above, in one embodiment,the calibration parameter (for example, the sensitivity of the sensor101 (FIG. 1)) is updated based on the updated lag corrected monitoreddata at calibration time (T=0) described above. In particular, in oneembodiment, the calibration parameter may be updated by determining theratio of the updated lag corrected monitored data at calibration time(T=0) and the reference value (for example, the capillary blood glucosevalue) determined at calibration time (T=0).

After updating the calibration parameter as described above, in oneembodiment, the lag corrected and calibrated monitored data at thesubsequent incremented time (T=1) is determined based on the updatedcalibration parameter value. For example, in one embodiment, themonitored sensor data at the subsequent incremented time (T=1) in oneembodiment may be divided by the updated sensitivity to determine thedynamically lag corrected and calibrated monitored sensor data at thesubsequent incremented time (T=1).

In another embodiment, the dynamically lag corrected and calibratedmonitored sensor data at the subsequent incremented time (T=1) may bedetermined based on the updated calibration parameter and thedynamically lag corrected monitored sensor data at the subsequentincremented time (T=1). In this case, the dynamically updated sensordata at the subsequent incremented time (T=1) in one embodiment may bedetermined by calculating the rate of change of the monitored data atthe subsequent incremented time (T=1) using similar filtering techniquesas described above, and applying the predetermined constant (forexample, the predetermined time constant discussed above), the result ofwhich is then added to the detected or monitored data at the subsequentincremented time (T=1). In other words, in one embodiment, thecalculated rate of change of the monitored data at the subsequentincremented time (T=1) is multiplied by the predetermined time constant,and the resulting value is added to the monitored data value at thesubsequent incremented time (T=1). This sum in one embodiment representsthe dynamically updated monitored sensor data at the subsequentincremented time (T=1).

In this manner, in one embodiment, lag correction of analyte sensor datamay be pseudo-retrospectively (or substantially in real time) updatedusing the monitored analyte data stream substantially continuouslydetected by the sensor 101 (FIG. 1) with the dynamic updating of thecalibration parameter. Thus, in one aspect, lag error or error due tolag compensation may be overcome by, for example, updating the sensorsensitivity retrospectively with each value of the detected or monitoredanalyte levels. Accordingly, in one embodiment, calibration inaccuraciesdue to change (for example, rapid acceleration) of analyte levels afterperforming discrete calibration may be mitigated by updating thecalibration routine taking into consideration the near immediate postcalibration analyte sensor data to obtain a more reliable and accuratevalue associated with the rate of change of the monitored analytelevels. In one embodiment, the overall system accuracy of the monitoredand detected analyte values may be improved.

FIG. 7 is a flowchart illustrating a method for estimating the lag timeconstant from sensor analyte data and reference measurements inaccordance with one embodiment of the present disclosure. This methodcan be employed in real-time or offline, or components of the method maybe one or the other. Referring to FIG. 7, measured analyte sensor datafor example, from a transcutaneously positioned analyte sensor may beobtained and/or retrieved from a memory or storage device operativelycoupled to the analyte sensor (710). Thereafter, a reference glucosedata measurement is performed and received, for example, from a bloodglucose meter (720). Each of the reference glucose data is paired withone or more measured analyte sensor data in the same time vicinity asthe reference data (730). That is, the pairing may be to select a singleanalyte sensor data closest in time to the time of the reference data.Alternatively, the pairing could be a number of analyte sensor dataoccurring before and after the reference data. Alternatively, thepairing could be between a value that is the result of a calculationusing a number of analyte sensor data occurring before and after thereference data; the calculation could be an average or some otherappropriate form of filtering.

Referring back to FIG. 7, thereafter, these pairs are used to estimatethe lag time constant (740). This estimate could be performed usingstandard system identification techniques. For instance, a 2 or moredimensional least squares technique could be used to estimate the timeconstant, along with the sensor sensitivity. The preferred embodimentfor estimating the time constant separates the estimation of thesensitivity from the estimation of the lag time constant, and isillustrated in FIG. 8, and described later.

Once the estimate of the lag time constant is determined, it may be usedto correct the sensor data for lag effects (750). In one aspect,calibration for lag effects may be corrected. In another aspect, eachcalibrated sensor data may be corrected for lag effects as describedbelow.

Given a calibrated sensor providing a continuous stream of interstitialglucose G_(i), lag correction routine used to improve calibration mayalso be used to provide a blood glucose estimate G_(b). For example,consider a simple continuous time domain model of bloodglucose-to-interstitial glucose dynamics:

τ{dot over (G)}_(i)(t)=−G _(i)(t)+G _(b)(t)

where τ is the time constant, and Ġ_(i) is the rate of change of glucoselevel G_(i). Given that glucose G_(i) is available or determined byanother module made possible by the latest calibration procedure, therate of change of the glucose level Ġ_(i) may be determined in one ormore retrospectively or in real-time. The time constant τ is availableor computed by another module.

One real-time example to obtain a sampled data estimate of the rate ofchange Ġ_(i) at any time n is to use the average first difference of thepast N sampled G_(i) data:

${\hat{\overset{.}{G}}(n)} \approx \frac{{\sum\limits_{n = 1}^{N}\; {G_{i}(n)}} - {G_{i}\left( {n - 1} \right)}}{{NT}_{s}}$

where T_(s) is the sample time or the time interval between the sampledvalues of G_(i)(n). The notation {circumflex over (Ġ)}_(i) signifiesthat it is an estimate for Ġ_(i). Other approaches to compute Ġ_(i)include any other Finite Impulse Response (FIR) filters, InfiniteImpulse Response (IIR) filters, Wiener filter, and Kalman filter.

Given these available and/or computed values, blood glucose estimateG_(b) can be computed by rearranging the blood glucose-to-interstitialglucose model in the following manner:

Ĝ _(b)(n)=G _(i)(n)+τ{circumflex over ({dot over (G)})}_(i)(n)

where n is any time instance. Note that for improved noise rejection,the first term on the right hand side, G_(i)(n), may be replaced by afiltered version. The filter can take any eligible forms such as the FIRfilter, IIR filter, Wiener filter, or the Kalman filter.

In one embodiment, steps 730 and 740 are performed as a batch process.For instance, the receiver unit 104, may perform the routines describedat steps 730 and 740 periodically or continuously, after each referencemeasurement, with a batch of data saved on the receiver unit 104. Aftereach batch process, the lag time constant may be updated and used forsubsequent real-time lag correction processes. Another embodiment mayinclude the batch process to be performed external to the receiver unitin a computer terminal, for example. After the time constant isestimated, it may be downloaded or transmitted to the receiver unit tobe used in subsequent real time lag correction processes.

In a further aspect, the routine illustrated in FIG. 7 may be performedretrospectively, for example, using a computer or server terminal. Thismay be used in applications where lag correction operations may beperformed retrospectively.

FIG. 8 illustrates estimation of the lag time constant from the pairs740 of FIG. 7 in one embodiment. The generation of a subset of pairs(810) may include generating a subset from pairs associated with sensordata absolute rate of change that is below a predetermined threshold. Anexample of the predetermined threshold includes 0.1 mg/dL/minute. Analternative predetermined threshold may be 6% change in the glucoseestimate per minute. That is, the lag effects may be minimized if therate of change is relatively low, and the subsequent estimation of thesensitivity (820) may be minimally affected by lag. Additionally, theincluded pairs may form a cluster of data in time. Other criteria forcreating a subset of pairs may include a) data from a single sensor, b)data from a single subject, c) data from groups of patients or users, d)data associated with any particular conditions, such as data where allglucose is above some value, e) data that meets certain data qualityconditions.

In the case where the criteria for generating a subset of pairs includesdetermining the glucose rate of change is below a predetermined glucoserate of change threshold, then the measured sensor data may be convertedto preliminary glucose estimates using a preliminary sensitivity. Thepreliminary sensitivity may be determined by a variety of ways,including: the use of a nominal sensitivity assigned at the factor for aparticular lot of sensors, the use of the median of the sensitivitiescalculated for each pair, or another suitable means. In the case wherethe criteria includes a percentage of signal change, then thedetermination of the preliminary sensitivity is not needed.

The pair subset criterion in one aspect may include a condition that thedata be from one sensor. In this embodiment, the sensitivity isestimated (820) for one sensor and the lag time constant is subsequentlyestimated (830-850). The receiver unit may subsequently use this timeconstant in real time to correct calibration and glucose calculationsfor lag effects. Further, this process may be repeated for multiplesensors, and the resulting multiple lag time constant estimates could becombined to create a single estimate used for lag correction. A simplecombination calculation may include an average; other combinationcalculations, such as an FIR filter or a median or a time weightedaverage.

The pair subset criterion may alternatively include that the data mustbe from one patient, over multiple sensors. This subset may be furthersegregated by sensor when determining sensor sensitivity (820) andapplying the determined sensitivity to the analyte sensor data (830).

Referring back to FIG. 8, the sensitivity of a subset of pairs isdetermined (820). In one aspect, a ratio for each reference data andassociated sensor data (or value representing a combination of sensordata) are determined, and the median from this set of ratios isdetermined.

The sensitivity estimated is applied to the analyte sensor data (830) togenerate the calibrated sensor data. Specifically, each sensor datapoint is divided by the sensitivity ratio to convert the sensor datafrom its native measurement units into glucose units. If the pairsubsets (810) include data from multiple sensors, then the sensitivityestimated for each sensor is applied to the sensor data associated withthat sensor.

Referring still to FIG. 8, the calibrated sensor data is paired withreference data (840). In a further aspect, another pair subset may becreated to only include pairs where the glucose absolute rate of changebased on the sensor exceeded a predetermined threshold, for example 0.75mg/dL. In this case, the pairs associated with low glucose rate ofchange may be minimally informative to the lag time constant estimation.This may not be necessary to produce a result, but may be useful toreduce the amount of computation needed if this is desirable.

In step 850, the pairs of calibrated sensor data and reference data areused to estimate the lag time constant. One embodiment of the timeconstant calculation is to assume that the interstitial fluid glucoselevel is connected to the blood glucose level by a simple first orderdifferential expression:

${\tau \frac{{dI}_{NAV}}{dt}} = {{- I_{NAV}} + {{SG}_{REF}.}}$

Here, τ, is the time constant, I_(NAV) is the sensor data measurement, Sis the ratio of sensor current to blood glucose concentration(sensitivity), and G_(REF) is a blood glucose reference measurement.This equation may be solved for τ if the values of the other quantitiescan be estimated. One approach for estimation of the sensor datameasurement and its time rate of change may require filtering of thesignal in order to reduce the effect of high frequency noise. In oneaspect, the sensor data may be fit in a time interval around thereference point, G_(REF), to a low order polynomial via the leastsquares algorithm. Another embodiment is to use a causal or noncausallow-pass filter.

If more than one reference point is available, then there will bemultiple values of the time constant τ, and in which case, a leastsquares approach may be applied

Accordingly, the identification of the sensitivity parameter may beisolated from that of the lag parameter. Moreover, in one aspect, dataexclusion may be in the routine illustrated in FIG. 8. In oneembodiment, a paired point exclusion may be added just after calibratedsensor data is paired with reference data, where the exclusion criteriamay be a constraint appropriate for improving the estimate of lag. Forinstance, if the lag time constant at high glucose values is desirableto determine, then a step could be added to exclude pairs associatedwith a reference below a predetermined glucose threshold.

In this manner, the time lag constant determination associated with ananalyte sensor for lag correction may be implemented in a real timemonitoring system such as one aspect of the analyte monitoring system100, or alternatively, may be used as an analysis too, for example, by apatient, a physician or a health care provider to improve the treatmentof the patient based on improved physiological data associated with thepatient with minimal errors.

Indeed, in one aspect, lag compensation of analyte sensor measurementsmay be tuned or adjusted to the particular analyte sensor in use by thepatient or the user, resulting in improved accuracy of the overallanalyte monitoring system 100.

It is to be noted that, each step or routine associated each of theflowcharts illustrating the various embodiments of the presentdisclosure as shown in FIG. 4-8 do not have to be performed in the ordershown in the Figures, unless described otherwise, and within the scopeof the present disclosure, one or more step or routine in one or morefigures described above may be performed or executed in an orderdifferent from the order shown in the respective figures.

Referring to the Figures above, in particular embodiments, the lagcorrection and calibration and updating of monitored sensor data may beperformed by one or more processing units of the one or more receiverunit (104, 106) the transmitter unit 102 or the data processingterminal/infusion section 105. In addition, the one or more of thetransmitter unit 102, the primary receiver unit 104, secondary receiverunit 106, or the data processing terminal/infusion section 105 may alsoincorporate a blood glucose meter functionality, such that, the housingof the respective one or more of the transmitter unit 102, the primaryreceiver unit 104, secondary receiver unit 106, or the data processingterminal/infusion section 105 may include a test strip port configuredto receive a blood sample for determining one or more blood glucoselevels of the patient.

In a further embodiment, the one or more of the transmitter unit 102,the primary receiver unit 104, secondary receiver unit 106, or the dataprocessing terminal/infusion section 105 may be configured to receivethe blood glucose value wirelessly over a communication link from, forexample, a glucose meter. In still a further embodiment, the user orpatient manipulating or using the analyte monitoring system 100 (FIG. 1)may manually input the blood glucose value using, for example, a userinterface (for example, a keyboard, keypad, and the like) incorporatedin the one or more of the transmitter unit 102, the primary receiverunit 104, secondary receiver unit 106, or the data processingterminal/infusion section 105.

A computer implemented method in accordance with one embodiment includesdetermining a rate of change of an analyte level monitored by an analytesensor below a predetermined threshold, calibrating analyte dataassociated with the monitored analyte level received from analyte sensorbased on a reference measurement, determining a time lag constantassociated with the calibrated analyte data, and performing lagcorrection of the calibrated analyte data based on the determined timelag constant.

The method may include outputting the lag corrected calibrated analytedata.

In one aspect, the reference measurement may include a blood glucosemeasurement.

The reference measurement may be obtained within a predetermined timeperiod relative to the monitored analyte level, where the predeterminedtime period may be less than approximately 30 minutes or greater thanapproximately 30 minutes, or any other suitable time range.

In one aspect, calibrating the analyte data may include determining asensitivity value associated with the analyte sensor, where thesensitivity value may include a ratio of the analyte data and thecorresponding reference measurement.

The analyte level may include glucose level.

In a further aspect, determining the rate of change may includedetermining a rate of increase or a rate of decrease of the monitoredanalyte level for a predefined time period.

An apparatus in accordance with one embodiment may include one or moreprocessing units, and a memory for storing instructions which, whenexecuted by the one or more processors, causes the one or moreprocessing units to determine a rate of change of an analyte levelmonitored by an analyte sensor below a predetermined threshold,calibrate analyte data associated with the monitored analyte levelreceived from analyte sensor based on a reference measurement, determinea time lag constant associated with the calibrated analyte data, andperform lag correction of the calibrated analyte data based on thedetermined time lag constant.

The memory may be further configured for storing instructions which,when executed by the one or more processing units, causes the one ormore processing units to output the lag corrected calibrated analytedata.

In a further aspect, the apparatus may include a communication moduleoperatively coupled to the one or more processing units, thecommunication module configured to receive the reference measurementwithin a predetermined time period relative to the monitored analytelevel, where the predetermined time period may be less thanapproximately 30 minutes or greater than approximately 30 minutes, orany other suitable time period.

In a further aspect, the memory may be further configured for storinginstructions which, when executed by the one or more processing units,causes the one or more processing units to determine a sensitivity valueassociated with the analyte sensor, where the sensitivity value mayinclude a ratio of the analyte data and the corresponding referencemeasurement.

The memory may be further configured for storing instructions which,when executed by the one or more processing units, causes the one ormore processing units to determine the rate of change based ondetermining a rate of increase or a rate of decrease of the monitoredanalyte level for a predefined time period.

A physiological monitoring device in still another aspect may includemeans for determining a rate of change of an analyte level monitored byan analyte sensor below a predetermined threshold, means for calibratinganalyte data associated with the monitored analyte level received fromanalyte sensor based on a reference measurement, means for determining atime lag constant associated with the calibrated analyte data, and meansfor performing lag correction of the calibrated analyte data based onthe determined time lag constant.

A method in accordance with one embodiment of the present disclosureincludes obtaining a reference data point at a first predetermined time,receiving a first data at the first predetermined time, calibrating thefirst data based on the reference data point, receiving a second data ata second predetermined time, updating the calibrated first data based onthe second data, and calibrating the second data.

The reference data point may include a blood glucose value.

The first predetermined time may include a calibration time associatedwith the calibration of one or more of the first data or the seconddata.

The first data and the second data may include a respective one of amonitored analyte value.

In one embodiment, calibrating the first data may include determining afirst rate of change of the first data at the first predetermined time,and performing a first lag compensation of the first data based on thefirst rate of change to generate a first lag compensated first data. Ina further embodiment, calibrating the first data may include determininga first calibration parameter associated with the first data based onthe reference data point and the first lag compensated first data, andgenerating a calibrated first data based on the first calibrationparameter and the first lag compensated first data.

Updating the calibrated first data in one embodiment may includedetermining a second rate of change of the first data at the firstpredetermined time based on the second data, and performing a second lagcompensation of the first data based on the second rate of change of thefirst data to generate a second lag compensated first data.

Also, calibrating the second data may include determining a secondcalibration parameter associated with the first data based on thereference data point and the second lag compensated first data, andgenerating a calibrated second data based on the second calibrationparameter and the second lag compensated first data.

A method in accordance with another embodiment may include determining acalibration parameter associated with a detected analyte value,calibrating the analyte value based on the calibration parameter, anddynamically updating the calibration parameter.

The method in another aspect may include calibrating a second detectedanalyte value based on the dynamically updated calibration parameter.

Further, dynamically updating the calibration parameter may also includedetermining a rate of change of the detected analyte value, andgenerating a lag compensated analyte value based on the rate of change.

In addition, calibrating the analyte value may further includedetermining a sensitivity associated with the detected analyte value,and applying the sensitivity to the lag compensated analyte value.

Moreover, in still another embodiment, dynamically updating thecalibration parameter may include updating the rate of change of thedetected analyte value, and updating the lag compensated analyte value,where updating the rate of change may include determining the rate ofchange of the detected analyte value between a first predetermined timeand a second predetermined time.

In still another embodiment, calibrating the analyte value may includedetecting a calibration data, determining a sensitivity based on thecalibration data and the lag compensated analyte value, and generating alag compensated and calibrated analyte value.

An apparatus in accordance with another embodiment may include one ormore processing units, and a memory for storing instructions which, whenexecuted by the one or more processors, causes the one or moreprocessing units to obtain a reference data point at a firstpredetermined time, receive a first data at the first predeterminedtime, calibrate the first data based on the reference data point;receive a second data at a second predetermined time; update thecalibrated first data based on the second data; and calibrate the seconddata.

The memory in another aspect may be configured for storing instructionswhich, when executed by the one or more processing units, causes the oneor more processing units to determine a first rate of change of thefirst data at the first predetermined time, and to perform a first lagcompensation of the first data based on the first rate of change togenerate a first lag compensated first data.

Moreover, the memory in yet another embodiment may be further configuredfor storing instructions which, when executed by the one or moreprocessing units, causes the one or more processing units to determine afirst calibration parameter associated with the first data based on thereference data point and the first lag compensated first data and togenerate a calibrated first data based on the first calibrationparameter and the first lag compensated first data.

Additionally, the memory may still be further configured for storinginstructions which, when executed by the one or more processing units,causes the one or more processing units to determine a second rate ofchange of the first data at the first predetermined time based on thesecond data, and to perform a second lag compensation of the first databased on the second rate of change of the first data to generate asecond lag compensated first data.

In yet still another aspect, the memory may be further configured forstoring instructions which, when executed by the one or more processingunits, causes the one or more processing units to determine a secondcalibration parameter associated with the first data based on thereference data point and the second lag compensated first data, and togenerate a calibrated second data based on the second calibrationparameter and the second lag compensated first data.

A method in accordance with still another embodiment of the presentdisclosure includes, dynamically, and in particular embodiments, inreal-time, obtaining reference data at a first predetermined time,receiving measured data prior to and including (or near) the firstpredetermined time, calculating a first calibration parameter (orparameters) using the data, calibrating the measured data based on thecalibration parameter, receiving measured data at a second predeterminedtime, updating the calibration parameter based on all of the previousdata and the newly received measured data, calibrating the newlyreceived measured data based on the updated calibration parameter, andrepeating a number of times the process of receiving new measurementdata, updating the calibration parameter, calibrating the newly receivedmeasurement data, and calibrating any newly received measurement datawith the fully updated calibration parameter.

A method in a further embodiment includes performing lag compensation onthe measured data that is used to update the calibration parameter. Lagcompensation may optionally be performed on the measured data that iscalibrated. A method in a further embodiment includes filtering themeasured data that is used to update the calibration parameter.

An apparatus in accordance with yet still another embodiment includesone or more processing units, and a memory for storing instructionswhich, when executed by the one or more processors, causes the one ormore processing units to dynamically, and in particular embodiments, inreal-time, obtain reference data at a first predetermined time, retrievemeasured data prior to and including (or near) the first predeterminedtime, calculate a first calibration parameter (or parameters) using thedata, calibrate the measured data based on the calibration parameter,retrieve measured data at a second predetermined time, update thecalibration parameter based on all of the previous data and the newlyreceived measured data, calibrate the newly received measured data basedon the updated calibration parameter, and repeat a number of times theprocess of receiving new measurement data, updating the calibrationparameter, calibrating the newly received measurement data, andcalibrating any newly received measurement data with the fully updatedcalibration parameter.

A method, in one embodiment, may comprise, determining a predeterminedtime period characterized with a rate of change of an analyte levelbelow a preset threshold, defining a data set associated with amonitored analyte level within the predetermined time period,determining a sensitivity value based on the defined data set, applyingthe determined sensitivity value to signals associated with themonitored analyte level including the defined data set, and estimating atime constant associated with an analyte sensor used to monitor theanalyte level.

The preset threshold associated with the rate of change of the analytelevel may be characterized by a quiescent state of the signals receivedfrom the analyte sensor.

In one aspect, the method may include receiving a reference data, andapplying the sensitivity value to the signals associated with themonitored analyte level based on the received reference data.

The reference data may include a reference blood glucose measurement.

The reference data may be substantially time corresponding to one ormore data in the defined data set.

The method may include calibrating the signals associated with themonitored analyte level based at least in part on the estimated timeconstant.

In another aspect, the method may include performing lag correction ofthe signals associated with the monitored analyte level based on theestimated time constant.

Further, the method may include calibrating the lag corrected signals toestimate the corresponding monitored glucose level.

In another embodiment, a computer implemented method may comprise,calibrating analyte data associated with a monitored analyte levelreceived from an analyte sensor based on a reference measurement,determining a lag time constant associated with the calibrated analytedata, and performing lag correction of the calibrated analyte data basedon the determined time lag constant.

In one aspect, the computer implemented method may include outputtingthe lag corrected calibrated analyte data.

The reference measurement may include a blood glucose measurement.

The reference measurement may be obtained within a predetermined timeperiod relative to the monitored analyte level.

The predetermined time period may be less than approximately 30 minutesor greater than approximately 30 minutes.

Calibrating the analyte data may include determining a sensitivity valueassociated with the analyte sensor.

The sensitivity value may include a ratio of the analyte data and thecorresponding reference measurement.

The analyte level may be a glucose level.

Determining the rate of change may include determining a rate ofincrease or a rate of decrease of the monitored analyte level for apredefined time period.

In one aspect, the computer implemented method may further includedetermining a rate of change of the analyte level monitored by theanalyte sensor.

The rate of change of the analyte level may be determined below apredetermined threshold.

The analyte sensor may be configured to measure the analyte level basedon coulometry.

The analyte sensor may be configured to measure the analyte level basedon amperometry.

In yet another embodiment, an apparatus may comprise, a datacommunication interface, one or more processors operatively coupled tothe data communication interface, and a memory for storing instructionswhich, when executed by the one or more processors, causes the one ormore processors to determine a predetermined time period characterizedwith a rate of change of an analyte level below a preset threshold,define a data set associated with a monitored analyte level within thepredetermined time period, determine a sensitivity value based on thedefined data set, apply the determined sensitivity value to signalsassociated with the monitored analyte level including the defined dataset, and estimate a time constant associated with an analyte sensor usedto monitor the analyte level.

Various other modifications and alterations in the structure and methodof operation of this invention will be apparent to those skilled in theart without departing from the scope and spirit of the invention.Although the invention has been described in connection with specificpreferred embodiments, it should be understood that the invention asclaimed should not be unduly limited to such specific embodiments. It isintended that the following claims define the scope of the presentdisclosure and that structures and methods within the scope of theseclaims and their equivalents be covered thereby.

What is claimed is:
 1. A method, comprising: determining a predetermined time period characterized with a rate of change of an analyte level below a preset threshold; defining a data set associated with a monitored analyte level within the predetermined time period; determining a sensitivity value based on the defined data set; applying the determined sensitivity value to signals associated with the monitored analyte level including the defined data set; and estimating a time constant associated with an analyte sensor used to monitor the analyte level. 